#!/usr/bin/env python
# coding: utf-8

# # Measuring Synaptic Short Term Plasticity
# 
# In this notebook we use the [Allen Institute synaptic physiology dataset](https://portal.brain-map.org/explore/connectivity/synaptic-physiology) to measure short term plasticity and its relationship to cell subclass.
# 
# For an introduction to the Jupyter Notebook interface interface, try [Codeacademy: How To Use Jupyter Notebooks]( https://www.codecademy.com/articles/how-to-use-jupyter-notebooks) or [Jupyter Notebook Quick Start Guide](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html).
# 

# In[1]:


import numpy as np
import scipy.stats
from aisynphys.database import SynphysDatabase
from aisynphys.cell_class import CellClass, classify_pair_dataframe


# ### 1. Load the data
# 
# First, load up the "small" database from release 1.0 and query all cell `pairs` for mouse projects.

# In[2]:


# Download and cache the sqlite file for the requested database
#   (for available versions, see SynphysDatabase.list_versions)
db = SynphysDatabase.load_current('small')

# Load all synapses associated with mouse multipatch experiments
pairs = db.pair_query(
    experiment_type='standard multipatch',
    species='mouse',
    synapse=True,
    preload=['cell', 'synapse'],
).dataframe()

print("loaded %d synapses" % len(pairs))


# ## 2. Classify cells
# 
# We will pick four simple class defintions for this analysis:

# In[3]:


cell_classes = {
    'pyr': CellClass(cell_class='ex', name='pyr'),
    'pvalb': CellClass(cre_type='pvalb', name='pvalb'),
    'sst': CellClass(cre_type='sst', name='sst'),
    'vip': CellClass(cre_type='vip', name='vip'),
}   


# In[4]:


# add new columns to the dataframe that apply the classification defined above
classify_pair_dataframe(cell_classes, pairs, col_names=('pre_class', 'post_class'))


# ## 3. Calculate average STP metric for each pre/post pairing

# In[5]:


stp = pairs.pivot_table('dynamics.stp_induction_50hz', 'pre_class', 'post_class', aggfunc=np.mean)

# sort rows/cols into the expected order
order = list(cell_classes)
stp = stp[order].loc[order]

stp


# In[6]:


import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')

fig,ax = plt.subplots(figsize=(8, 6))

hm = sns.heatmap(stp, cmap='coolwarm', vmin=-0.4, vmax=0.4, square=True, ax=ax,
                cbar_kws={"ticks":[-0.3, 0, 0.3], 'label': '<-- depressing       facilitating -->'})

fig.suptitle("50 Hz Train-induced STP", fontsize=16)
hm.set_xlabel("postsynaptic", fontsize=14)
hm.set_ylabel("presynaptic", fontsize=14);
hm.figure.axes[-1].yaxis.label.set_size(14)
hm.tick_params(labelsize=12)


# ## Alternatively, we can calculate the average resting state variance for each pairing

# In[7]:


var = pairs.pivot_table('dynamics.variability_resting_state', 'pre_class', 'post_class', aggfunc=np.mean)

# sort rows/cols into the expected order
order = list(cell_classes)
var = var[order].loc[order]

var


# In[8]:


fig,ax = plt.subplots(figsize=(8, 6))

hm = sns.heatmap(var, cmap='viridis', vmin=-1.5, vmax=1.5, square=True, ax=ax,
                cbar_kws={"ticks":[-1.5, 0, 1.5], 'label': '<-- low variance        high variance -->'})

fig.suptitle("Resting State Variability", fontsize=16)
hm.set_xlabel("postsynaptic", fontsize=14)
hm.set_ylabel("presynaptic", fontsize=14);
hm.figure.axes[-1].yaxis.label.set_size(14)
hm.tick_params(labelsize=12)


# ## 4. Extract multiple synaptic properties and plot as matrices

# Plotting synaptic properties among cell sublcasses in matrices like these is common enough that we have provided a convenince function to do just that called `cell_class_matrix`

# In[9]:


from aisynphys.ui.notebook import cell_class_matrix

fig,ax = plt.subplots(1, 3, figsize=(16, 3))

metrics = ['stp_induction_50hz', 'stp_recovery_250ms', 'variability_resting_state']

for i, metric in enumerate(metrics):
    cell_class_matrix(
        pre_classes=cell_classes, 
        post_classes=cell_classes,
        metric=metric, 
        class_labels=None, ax=ax[i],
        db=db, pair_query_args={
            'experiment_type': 'standard multipatch',
            'synapse': True,
            'species': 'mouse',
        }
    );


# In[ ]: